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Wu-chun Feng
Wu-Chun Feng
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2020 – today
- 2025
- [j51]Vignesh Adhinarayanan
, Wu-chun Feng:
Looking Back to Look Forward: 15 Years of the Green500. Computer 58(1): 76-86 (2025) - 2024
- [j50]Frank Wanye
, Vitaliy Gleyzer
, Edward K. Kao
, Wu-chun Feng
:
SamBaS: Sampling-Based Stochastic Block Partitioning. IEEE Trans. Netw. Sci. Eng. 11(3): 3053-3065 (2024) - [c217]Saikat Dey
, Sonal Jha
, Wu-chun Feng
:
G2A2: Graph Generator with Attributes and Anomalies. CF 2024 - [c216]Wu-chun Feng
, Xuewen Cui
, Thomas Scogland
, Bronis R. de Supinski
:
BLP: Block-Level Pipelining for GPUs. CF 2024 - [c215]Niteya Shah, Christine Sweeney, Vinay Ramakrishnaiah, Jeffrey Donatelli, Wu-Chun Feng:
Optimizing and Scaling the 3D Reconstruction of Single-Particle Imaging. IPDPS 2024: 253-264 - 2023
- [c214]Sonal Jha
, Wu-chun Feng
:
GRAPPEL: A Graph-based Approach for Early Risk Assessment of Acute Hypertension in Critical Care. BCB 2023: 36:1-36:6 - [c213]Frank Wanye, Vitaliy Gleyzer, Edward K. Kao, Wu-Chun Feng:
Exact Distributed Stochastic Block Partitioning. CLUSTER 2023: 25-36 - [c212]Frank Wanye, Wu-Chun Feng:
On the Multi-Dimensional Acceleration of Stochastic Blockmodeling for Community Detection. CLUSTER Workshops 2023: 70-71 - [c211]Atharva Gondhalekar, Wu-chun Feng:
On the Three P's of Parallel Programming for Heterogeneous Computing: Performance, Productivity, and Portability. HPEC 2023: 1-7 - [c210]Frank Wanye, Vitaliy Gleyzer, Edward K. Kao, Wu-chun Feng:
An Integrated Approach for Accelerating Stochastic Block Partitioning. HPEC 2023: 1-7 - [i7]Frank Wanye, Vitaliy Gleyzer, Edward K. Kao, Wu-chun Feng:
Exact Distributed Stochastic Block Partitioning. CoRR abs/2305.18663 (2023) - 2022
- [c209]Atharva Gondhalekar, Thomas Twomey, Wu-chun Feng:
On the Characterization of the Performance-Productivity Gap for FPGA. HPEC 2022: 1-8 - [c208]Paul Sathre, Atharva Gondhalekar, Wu-chun Feng:
Edge-Connected Jaccard Similarity for Graph Link Prediction on FPGA. HPEC 2022: 1-10 - [c207]Karim Youssef, Abdullah Al Raqibul Islam, Keita Iwabuchi, Wu-chun Feng, Roger Pearce:
Optimizing Performance and Storage of Memory-Mapped Persistent Data Structures. HPEC 2022: 1-7 - [c206]Karim Youssef, Niteya Shah, Maya B. Gokhale, Roger Pearce, Wu-chun Feng:
AutoPager: Auto-tuning Memory-Mapped I/O Parameters in Userspace. HPEC 2022: 1-7 - [c205]Frank Wanye
, Vitaliy Gleyzer, Edward K. Kao, Wu-chun Feng:
On the Parallelization of MCMC for Community Detection. ICPP 2022: 87:1-87:13 - [i6]Saikat Dey, Sonal Jha, Wu-chun Feng:
G2A2: An Automated Graph Generator with Attributes and Anomalies. CoRR abs/2210.07449 (2022) - 2021
- [j49]Xuewen Cui
, Wu-chun Feng:
IterML: Iterative Machine Learning for Intelligent Parameter Pruning and Tuning in Graphics Processing Units. J. Signal Process. Syst. 93(4): 391-403 (2021) - [c204]Karim Youssef, Keita Iwabuchi, Wu-Chun Feng, Roger Pearce:
Privateer: Multi-versioned Memory-mapped Data Stores for High-Performance Data Science. HPEC 2021: 1-7 - [c203]Sajal Dash
, Qais Al-Hajri, Wu-chun Feng, Harold R. Garner, Ramu Anandakrishnan:
Scaling Out a Combinatorial Algorithm for Discovering Carcinogenic Gene Combinations to Thousands of GPUs. IPDPS 2021: 837-846 - [c202]Sajal Dash
, Junqi Yin, Mallikarjun Shankar, Feiyi Wang, Wu-chun Feng:
Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary. DRBSD@SC 2021: 11-18 - [i5]Frank Wanye, Vitaliy Gleyzer, Edward K. Kao, Wu-chun Feng:
Topology-Guided Sampling for Fast and Accurate Community Detection. CoRR abs/2108.06651 (2021) - [i4]Garvit Goel, Jingyuan Qi, Wu-chun Feng, Guohua Cao:
A Deep-Learning Framework for Improving COVID-19 CT Image Quality and Diagnostic Accuracy. CoRR abs/2112.09216 (2021) - 2020
- [j48]Moeti Masiane, Anne Driscoll, Wu-chun Feng, John E. Wenskovitch
, Chris North:
Towards insight-driven sampling for big data visualisation. Behav. Inf. Technol. 39(7): 788-807 (2020) - [c201]Vignesh Adhinarayanan, Wu-chun Feng:
Approximate Pattern Matching for On-Chip Interconnect Traffic Prediction. PACT 2020: 357-358 - [c200]Sarunya Pumma, Daniele Buono, Fabio Checconi, Xinyu Que, Wu-chun Feng:
Alleviating Load Imbalance in Data Processing for Large-Scale Deep Learning. CCGRID 2020: 262-271 - [c199]Karim Youssef, Wu-chun Feng:
SparkLeBLAST: Scalable Parallelization of BLAST Sequence Alignment Using Spark. CCGRID 2020: 539-548 - [c198]Atharva Gondhalekar, Wu-Chun Feng:
Exploring FPGA Optimizations in OpenCL for Breadth-First Search on Sparse Graph Datasets. FPL 2020: 133-137 - [c197]Wu-chun Feng, Da Zhang, Jing Zhang, Kaixi Hou, Sarunya Pumma, Hao Wang:
A Feasibility Study for MPI over HDFS. HPEC 2020: 1-7 - [c196]Paul Sathre, Atharva Gondhalekar, Mohamed W. Hassan, Wu-Chun Feng:
MetaCL: Automated "Meta" OpenCL Code Generation for High-Level Synthesis on FPGA. HPEC 2020: 1-8 - [c195]Gregory D. Abram, Vignesh Adhinarayanan, Wu-chun Feng, David H. Rogers, James P. Ahrens
:
ETH: An Architecture for Exploring the Design Space of In-situ Scientific Visualization. IPDPS 2020: 515-526
2010 – 2019
- 2019
- [j47]Sarunya Pumma, Min Si, Wu-Chun Feng, Pavan Balaji:
Scalable Deep Learning via I/O Analysis and Optimization. ACM Trans. Parallel Comput. 6(2): 6:1-6:34 (2019) - [j46]Xiaodong Yu, Hao Wang, Wu-chun Feng, Hao Gong, Guohua Cao:
GPU-Based Iterative Medical CT Image Reconstructions. J. Signal Process. Syst. 91(3-4): 321-338 (2019) - [c194]Ahmed E. Helal, Ashwin M. Aji, Michael L. Chu, Bradford M. Beckmann, Wu-chun Feng:
Adaptive Task Aggregation for High-Performance Sparse Solvers on GPUs. PACT 2019: 324-336 - [c193]Xuewen Cui
, Wu-chun Feng:
Iterative machine learning (IterML) for effective parameter pruning and tuning in accelerators. CF 2019: 16-23 - [c192]Paul Sathre, Mark K. Gardner, Wu-chun Feng:
On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation. HPC Asia 2019: 1-8 - [c191]Mohamed W. Hassan, Scott Pakin
, Wu-chun Feng:
C to D-Wave: A High-level C Compilation Framework for Quantum Annealers. HPEC 2019: 1-8 - [c190]Frank Wanye
, Vitaliy Gleyzer, Wu-chun Feng:
Fast Stochastic Block Partitioning via Sampling. HPEC 2019: 1-7 - 2018
- [j45]Kaixi Hou
, Hao Wang
, Wu-Chun Feng:
A Framework for the Automatic Vectorization of Parallel Sort on x86-Based Processors. IEEE Trans. Parallel Distributed Syst. 29(5): 958-972 (2018) - [c189]Jing Zhang, Ashwin M. Aji, Michael L. Chu, Hao Wang, Wu-chun Feng:
Taming irregular applications via advanced dynamic parallelism on GPUs. CF 2018: 146-154 - [c188]Bishwajit Dutta, Vignesh Adhinarayanan, Wu-chun Feng:
GPU power prediction via ensemble machine learning for DVFS space exploration. CF 2018: 240-243 - [c187]Ahmed E. Helal, Changhee Jung, Wu-chun Feng, Yasser Y. Hanafy:
CommAnalyzer: automated estimation of communication cost and scalability on HPC clusters from sequential code. HPDC 2018: 80-91 - [c186]Konstantinos Krommydas, Paul Sathre, Ruchira Sasanka, Wu-chun Feng:
A Framework for Auto-Parallelization and Code Generation: An Integrative Case Study with Legacy FORTRAN Codes. ICPP 2018: 57:1-57:10 - [c185]Kaixi Hou, Hao Wang, Wu-chun Feng, Jeffrey S. Vetter, Seyong Lee
:
Highly Efficient Compensation-Based Parallelism for Wavefront Loops on GPUs. IPDPS 2018: 276-285 - [c184]Vignesh Adhinarayanan, Bishwajit Dutta, Wu-chun Feng:
Making a Case for Green High-Performance Visualization Via Embedded Graphics Processors. IPDPS Workshops 2018: 721-724 - [c183]Mohamed W. Hassan, Ahmed E. Helal, Peter M. Athanas, Wu-Chun Feng, Yasser Y. Hanafy:
Exploring FPGA-specific Optimizations for Irregular OpenCL Applications. ReConFig 2018: 1-8 - [c182]Paul Sathre, Ahmed E. Helal, Wu-chun Feng:
A Composable Workflow for Productive Heterogeneous Computing on FPGAs via Whole-Program Analysis and Transformation. ReConFig 2018: 1-8 - 2017
- [j44]Sarunya Pumma, Wu-chun Feng, Phond Phunchongharn, Sylvain Chapeland, Tiranee Achalakul:
A runtime estimation framework for ALICE. Future Gener. Comput. Syst. 72: 65-77 (2017) - [j43]Annette C. Feng, Mark K. Gardner, Wu-chun Feng:
Parallel programming with pictures is a Snap! J. Parallel Distributed Comput. 105: 150-162 (2017) - [j42]Jing Zhang, Hao Wang, Wu-chun Feng:
cuBLASTP: Fine-Grained Parallelization of Protein Sequence Search on CPU+GPU. IEEE ACM Trans. Comput. Biol. Bioinform. 14(4): 830-843 (2017) - [c181]Xiaodong Yu, Kaixi Hou, Hao Wang, Wu-chun Feng:
Robotomata: A framework for approximate pattern matching of big data on an automata processor. IEEE BigData 2017: 283-292 - [c180]Xiaodong Yu, Hao Wang, Wu-chun Feng, Hao Gong, Guohua Cao:
An Enhanced Image Reconstruction Tool for Computed Tomography on CPUs. Conf. Computing Frontiers 2017: 97-106 - [c179]Kaixi Hou, Hao Wang, Wu-chun Feng:
GPU-UniCache: Automatic Code Generation of Spatial Blocking for Stencils on CPUs. Conf. Computing Frontiers 2017: 107-116 - [c178]Anshuman Verma, Huiyang Zhou
, Skip Booth, Robbie King, James Coole, Andy Keep, John Marshall, Wu-chun Feng:
Developing Dynamic Profiling and Debugging Support in OpenCL for FPGAs. DAC 2017: 56:1-56:6 - [c177]Sajal Dash, Anshuman Verma, Chris North, Wu-chun Feng:
Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis. HPCC/SmartCity/DSS 2017: 10-17 - [c176]Sarunya Pumma, Min Si, Wu-chun Feng, Pavan Balaji:
Towards Scalable Deep Learning via I/O Analysis and Optimization. HPCC/SmartCity/DSS 2017: 223-230 - [c175]Sarunya Pumma, Min Si, Wu-chun Feng, Pavan Balaji:
Parallel I/O Optimizations for Scalable Deep Learning. ICPADS 2017: 720-729 - [c174]Marziyeh Nourian, Xiang Wang, Xiaodong Yu, Wu-chun Feng, Michela Becchi:
Demystifying automata processing: GPUs, FPGAs or Micron's AP? ICS 2017: 1:1-1:11 - [c173]Kaixi Hou, Weifeng Liu
, Hao Wang
, Wu-chun Feng:
Fast segmented sort on GPUs. ICS 2017: 12:1-12:10 - [c172]Ahmed E. Helal, Wu-chun Feng, Changhee Jung, Yasser Y. Hanafy:
AutoMatch: An automated framework for relative performance estimation and workload distribution on heterogeneous HPC systems. IISWC 2017: 32-42 - [c171]Xiaodong Yu, Kaixi Hou, Hao Wang, Wu-chun Feng:
A framework for fast and fair evaluation of automata processing hardware. IISWC 2017: 120-121 - [c170]Jing Zhang, Sanchit Misra, Hao Wang, Wu-chun Feng:
Eliminating Irregularities of Protein Sequence Search on Multicore Architectures. IPDPS 2017: 62-71 - [c169]Xuewen Cui
, Thomas R. W. Scogland, Bronis R. de Supinski, Wu-chun Feng:
Directive-Based Partitioning and Pipelining for Graphics Processing Units. IPDPS 2017: 575-584 - [c168]Hao Wang, Jing Zhang, Da Zhang, Sarunya Pumma, Wu-chun Feng:
PaPar: A Parallel Data Partitioning Framework for Big Data Applications. IPDPS 2017: 605-614 - [c167]Kaixi Hou, Wu-chun Feng, Shuai Che:
Auto-Tuning Strategies for Parallelizing Sparse Matrix-Vector (SpMV) Multiplication on Multi- and Many-Core Processors. IPDPS Workshops 2017: 713-722 - [c166]Vignesh Adhinarayanan
, Wu-chun Feng, David H. Rogers, James P. Ahrens
, Scott Pakin:
Characterizing and Modeling Power and Energy for Extreme-Scale In-Situ Visualization. IPDPS 2017: 978-987 - 2016
- [j41]Jing Zhang, Sanchit Misra, Hao Wang, Wu-chun Feng:
muBLASTP: database-indexed protein sequence search on multicore CPUs. BMC Bioinform. 17: 443:1-443:14 (2016) - [j40]Ashwin M. Aji, Antonio J. Peña
, Pavan Balaji, Wu-chun Feng:
MultiCL: Enabling automatic scheduling for task-parallel workloads in OpenCL. Parallel Comput. 58: 37-55 (2016) - [j39]Xiaokui Shu, Jing Zhang, Danfeng (Daphne) Yao
, Wu-chun Feng:
Fast Detection of Transformed Data Leaks. IEEE Trans. Inf. Forensics Secur. 11(3): 528-542 (2016) - [j38]Ashwin M. Aji, Lokendra S. Panwar, Feng Ji, Karthik Murthy, Milind Chabbi, Pavan Balaji, Keith R. Bisset, James Dinan, Wu-chun Feng, John M. Mellor-Crummey
, Xiaosong Ma, Rajeev Thakur
:
MPI-ACC: Accelerator-Aware MPI for Scientific Applications. IEEE Trans. Parallel Distributed Syst. 27(5): 1401-1414 (2016) - [j37]Konstantinos Krommydas, Wu-chun Feng, Christos D. Antonopoulos
, Nikolaos Bellas
:
OpenDwarfs: Characterization of Dwarf-Based Benchmarks on Fixed and Reconfigurable Architectures. J. Signal Process. Syst. 85(3): 373-392 (2016) - [c165]Xiaodong Yu, Wu-chun Feng, Danfeng (Daphne) Yao
, Michela Becchi:
O3FA: A Scalable Finite Automata-based Pattern-Matching Engine for Out-of-Order Deep Packet Inspection. ANCS 2016: 1-11 - [c164]Konstantinos Krommydas, Ruchira Sasanka, Wu-chun Feng:
Bridging the FPGA programmability-portability Gap via automatic OpenCL code generation and tuning. ASAP 2016: 213-218 - [c163]Ramu Anandakrishnan
, Mayank Daga, Alexey Onufriev
, Wu-chun Feng:
Multiscale Approximation with Graphical Processing Units for Multiplicative Speedup in Molecular Dynamics. BCB 2016: 453-462 - [c162]Xiaodong Yu, Hao Wang, Wu-chun Feng, Hao Gong, Guohua Cao:
cuART: Fine-Grained Algebraic Reconstruction Technique for Computed Tomography Images on GPUs. CCGrid 2016: 165-168 - [c161]Vignesh Adhinarayanan
, Balaji Subramaniam, Wu-chun Feng:
Online Power Estimation of Graphics Processing Units. CCGrid 2016: 245-254 - [c160]Xuewen Cui
, Thomas R. W. Scogland, Bronis R. de Supinski, Wu-Chun Feng:
Directive-Based Pipelining Extension for OpenMP. CLUSTER 2016: 481-484 - [c159]Konstantinos Krommydas, Ahmed E. Helal, Anshuman Verma, Wu-chun Feng:
Bridging the Performance-Programmability Gap for FPGAs via OpenCL: A Case Study with OpenDwarfs. FCCM 2016: 198 - [c158]Konstantinos Krommydas, Wu-Chun Feng:
Telescoping Architectures: Evaluating Next-Generation Heterogeneous Computing. HiPC 2016: 162-171 - [c157]Hao Wang
, Weifeng Liu
, Kaixi Hou, Wu-chun Feng:
Parallel Transposition of Sparse Data Structures. ICS 2016: 33:1-33:13 - [c156]Vignesh Adhinarayanan
, Indrani Paul, Joseph L. Greathouse, Wei Huang, Ashutosh Pattnaik, Wu-chun Feng:
Measuring and modeling on-chip interconnect power on real hardware. IISWC 2016: 23-33 - [c155]Kaixi Hou, Hao Wang, Wu-chun Feng:
AAlign: A SIMD Framework for Pairwise Sequence Alignment on x86-Based Multi-and Many-Core Processors. IPDPS 2016: 780-789 - [c154]Annette C. Feng, Wu-chun Feng:
Parallel Programming with Pictures in a Snap! IPDPS Workshops 2016: 950-957 - [c153]Chung-Hsing Hsu, Wu-chun Feng:
The Right Metric for Efficient Supercomputing: A Ten-Year Retrospective. IPDPS Workshops 2016: 1090-1093 - [c152]Vignesh Adhinarayanan
, Wu-chun Feng:
An automated framework for characterizing and subsetting GPGPU workloads. ISPASS 2016: 307-317 - [c151]Islam Harb, Wu-Chun Feng:
Characterizing Performance and Power towards Efficient Synchronization of GPU Kernels. MASCOTS 2016: 451-456 - [c150]Ahmed E. Helal, Paul Sathre, Wu-chun Feng:
MetaMorph: a library framework for interoperable kernels on multi- and many-core clusters. SC 2016: 119-129 - 2015
- [j36]Juan Antonio Gómez Pulido
, Bertil Schmidt, Wu-chun Feng:
Accelerating Bioinformatics Applications via Emerging Parallel Computing Systems. IEEE ACM Trans. Comput. Biol. Bioinform. 12(5): 971-972 (2015) - [j35]Thomas R. W. Scogland, Wu-chun Feng, Barry Rountree, Bronis R. de Supinski:
CoreTSAR: Core Task-Size Adapting Runtime. IEEE Trans. Parallel Distributed Syst. 26(11): 2970-2983 (2015) - [c149]Ashwin Mandayam Aji, Antonio J. Peña
, Pavan Balaji, Wu-chun Feng:
Automatic Command Queue Scheduling for Task-Parallel Workloads in OpenCL. CLUSTER 2015: 42-51 - [c148]Xiaokui Shu, Jing Zhang, Danfeng Yao
, Wu-chun Feng:
Rapid Screening of Transformed Data Leaks with Efficient Algorithms and Parallel Computing. CODASPY 2015: 147-149 - [c147]Da Zhang, Hao Wang, Kaixi Hou, Jing Zhang, Wu-chun Feng:
pDindel: Accelerating indel detection on a multicore CPU architecture with SIMD. ICCABS 2015: 1-6 - [c146]Konstantinos Krommydas, Ruchira Sasanka, Wu-chun Feng:
GLAF: A Visual Programming and Auto-tuning Framework for Parallel Computing. ICPP 2015: 859-868 - [c145]Kaixi Hou, Hao Wang, Wu-chun Feng:
ASPaS: A Framework for Automatic SIMDization of Parallel Sorting on x86-based Many-core Processors. ICS 2015: 383-392 - [c144]Xiaokui Shu, Jing Zhang, Danfeng Yao
, Wu-chun Feng:
Rapid and parallel content screening for detecting transformed data exposure. INFOCOM Workshops 2015: 191-196 - [c143]Wu-chun Feng, Barry Rountree:
HPPAC Introduction and Committees. IPDPS Workshops 2015: 848 - [c142]Rubasri Kalidas, Mayank Daga, Konstantinos Krommydas, Wu-chun Feng:
On the Performance, Energy, and Power of Data-Access Methods in Heterogeneous Computing Systems. IPDPS Workshops 2015: 871-879 - [c141]Vignesh Adhinarayanan
, Wu-chun Feng, Jonathan Woodring, David H. Rogers, James P. Ahrens
:
On the Greenness of In-Situ and Post-Processing Visualization Pipelines. IPDPS Workshops 2015: 880-887 - [c140]Thomas R. W. Scogland, Wu-chun Feng:
Design and Evaluation of Scalable Concurrent Queues for Many-Core Architectures. ICPE 2015: 63-74 - [i3]Balaji Subramaniam, Wu-chun Feng:
Towards Energy-Proportional Computing Using Subsystem-Level Power Management. CoRR abs/1501.02724 (2015) - [i2]Balaji Subramaniam, Wu-chun Feng:
On the Energy Proportionality of Scale-Out Workloads. CoRR abs/1501.02729 (2015) - 2014
- [j34]Jiangling Yin, Junyao Zhang, Jun Wang
, Wu-chun Feng:
SDAFT: A novel scalable data access framework for parallel BLAST. Parallel Comput. 40(10): 697-709 (2014) - [c139]Thomas R. W. Scogland, Wu-Chun Feng:
Locality-aware memory association for multi-target worksharing in OpenMP. PACT 2014: 515-516 - [c138]Konstantinos Krommydas, Wu-chun Feng, Muhsen Owaida, Christos D. Antonopoulos
, Nikolaos Bellas
:
On the characterization of OpenCL dwarfs on fixed and reconfigurable platforms. ASAP 2014: 153-160 - [c137]Balaji Subramaniam, Wu-chun Feng:
Enabling Efficient Power Provisioning for Enterprise Applications. CCGRID 2014: 71-80 - [c136]Thomas R. W. Scogland, Wu-chun Feng:
Runtime Adaptation for Autonomic Heterogeneous Computing. CCGRID 2014: 562-565 - [c135]Carlo C. del Mundo, Wu-chun Feng:
Towards a performance-portable FFT library for heterogeneous computing. Conf. Computing Frontiers 2014: 11:1-11:10 - [c134]Jiangling Yin, Jun Wang
, Wu-chun Feng, Xuhong Zhang, Junyao Zhang:
SLAM: scalable locality-aware middleware for I/O in scientific analysis and visualization. HPDC 2014: 257-260 - [c133]Nataliya Timoshevskaya, Wu-chun Feng:
SAIS-OPT: On the characterization and optimization of the SA-IS algorithm for suffix array construction. ICCABS 2014: 1-6 - [c132]Kaixi Hou, Hao Wang, Wu-chun Feng:
Delivering Parallel Programmability to the Masses via the Intel MIC Ecosystem: A Case Study. ICPP Workshops 2014: 273-282 - [c131]Jing Zhang, Hao Wang, Heshan Lin, Wu-chun Feng:
cuBLASTP: Fine-Grained Parallelization of Protein Sequence Search on a GPU. IPDPS 2014: 251-260 - [c130]James E. McClure, Hao Wang, Jan F. Prins, Cass T. Miller, Wu-chun Feng:
Petascale Application of a Coupled CPU-GPU Algorithm for Simulation and Analysis of Multiphase Flow Solutions in Porous Medium Systems. IPDPS 2014: 583-592 - [c129]Vignesh Adhinarayanan
, Thaddeus Koehn, Krzysztof Kepa
, Wu-chun Feng, Peter Athanas:
On the performance and energy efficiency of FPGAs and GPUs for polyphase channelization. ReConFig 2014: 1-7 - [c128]Balaji Subramaniam, Wu-chun Feng:
On the Energy Proportionality of Distributed NoSQL Data Stores. PMBS@SC 2014: 264-274 - [c127]Thomas R. W. Scogland, Wu-chun Feng, Barry Rountree, Bronis R. de Supinski:
CoreTSAR: Adaptive Worksharing for Heterogeneous Systems. ISC 2014: 172-186 - [c126]Mohamed Nabeel, Nabanita Maji, Jing Zhang, Nataliya Timoshevskaya, Wu-chun Feng:
Aeromancer: A Workflow Manager for Large-Scale MapReduce-Based Scientific Workflows. TrustCom 2014: 739-746 - [c125]Thomas R. W. Scogland, Craig P. Steffen
, Torsten Wilde, Florent Parent
, Susan Coghlan, Natalie J. Bates, Wu-chun Feng, Erich Strohmaier:
A power-measurement methodology for large-scale, high-performance computing. ICPE 2014: 149-159 - 2013
- [j33]